






"""simple docstring"""


import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
		import torch

if is_vision_available():
		from PIL import Image

		from transformers import MobileNetVaImageProcessor




class 						__UpperCamelCase     (     unittest.TestCase						):
					def __init__(							self							:	Union[str, Any]     , UpperCAmelCase							:	str     , UpperCAmelCase							:	List[Any]=7     , UpperCAmelCase							:	Optional[Any]=3     , UpperCAmelCase							:	Dict=18     , UpperCAmelCase							:	Optional[Any]=30     , UpperCAmelCase							:	Any=400     , UpperCAmelCase							:	int=True     , UpperCAmelCase							:	Optional[int]=None     , UpperCAmelCase							:	Any=True     , UpperCAmelCase							:	List[str]=None     , )	->       Union[str, Any]:
											lowerCAmelCase   :List[Any]						=			size if size is not None else {'shortest_edge': 20}
											lowerCAmelCase   :str						=			crop_size if crop_size is not None else {'height': 18, 'width': 18}
											lowerCAmelCase   :Dict						=			parent
											lowerCAmelCase   :List[Any]						=			batch_size
											lowerCAmelCase   :Any						=			num_channels
											lowerCAmelCase   :Any						=			image_size
											lowerCAmelCase   :List[Any]						=			min_resolution
											lowerCAmelCase   :Dict						=			max_resolution
											lowerCAmelCase   :Optional[Any]						=			do_resize
											lowerCAmelCase   :str						=			size
											lowerCAmelCase   :int						=			do_center_crop
											lowerCAmelCase   :int						=			crop_size
					def        UpperCAmelCase__ (							self							:	List[str]   )	->       List[str]:
											return {
											    "do_resize": self.do_resize,
											    "size": self.size,
											    "do_center_crop": self.do_center_crop,
											    "crop_size": self.crop_size,
											}




@require_torch
@require_vision
class 						__UpperCamelCase     (     UpperCamelCase   ,     unittest.TestCase						):
					lowercase_    :				List[str]      =  MobileNetVaImageProcessor if is_vision_available() else None
					def        UpperCAmelCase__ (							self							:	Optional[Any]   )	->       Optional[Any]:
											lowerCAmelCase   :Optional[Any]						=			MobileNetVaImageProcessingTester(self   )
					@property
					def        UpperCAmelCase__ (							self							:	List[Any]   )	->       Any:
											return self.image_processor_tester.prepare_image_processor_dict()
					def        UpperCAmelCase__ (							self							:	Optional[int]   )	->       Any:
											lowerCAmelCase   :Any						=			self.image_processing_class(**self.image_processor_dict   )
											self.assertTrue(hasattr(UpperCAmelCase     , 'do_resize'   )   )
											self.assertTrue(hasattr(UpperCAmelCase     , 'size'   )   )
											self.assertTrue(hasattr(UpperCAmelCase     , 'do_center_crop'   )   )
											self.assertTrue(hasattr(UpperCAmelCase     , 'crop_size'   )   )
					def        UpperCAmelCase__ (							self							:	Dict   )	->       Any:
											lowerCAmelCase   :List[str]						=			self.image_processing_class.from_dict(self.image_processor_dict   )
											self.assertEqual(image_processor.size     , {'shortest_edge': 20}   )
											self.assertEqual(image_processor.crop_size     , {'height': 18, 'width': 18}   )

											lowerCAmelCase   :Any						=			self.image_processing_class.from_dict(self.image_processor_dict     , size=42     , crop_size=84   )
											self.assertEqual(image_processor.size     , {'shortest_edge': 42}   )
											self.assertEqual(image_processor.crop_size     , {'height': 84, 'width': 84}   )
					def        UpperCAmelCase__ (							self							:	Tuple   )	->       Dict:
											pass
					def        UpperCAmelCase__ (							self							:	Dict   )	->       int:
											# Initialize image_processing
											lowerCAmelCase   :Dict						=			self.image_processing_class(**self.image_processor_dict   )
											# create random PIL images
											lowerCAmelCase   :Tuple						=			prepare_image_inputs(self.image_processor_tester     , equal_resolution=UpperCAmelCase   )
											for image in image_inputs:
																	self.assertIsInstance(UpperCAmelCase     , Image.Image   )

											# Test not batched input
											lowerCAmelCase   :List[str]						=			image_processing(image_inputs[0]     , return_tensors='pt'   ).pixel_values
											self.assertEqual(
											    encoded_images.shape     , (
											        1,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.crop_size['height'],
											        self.image_processor_tester.crop_size['width'],
											    )     , )

											# Test batched
											lowerCAmelCase   :Dict						=			image_processing(UpperCAmelCase     , return_tensors='pt'   ).pixel_values
											self.assertEqual(
											    encoded_images.shape     , (
											        self.image_processor_tester.batch_size,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.crop_size['height'],
											        self.image_processor_tester.crop_size['width'],
											    )     , )
					def        UpperCAmelCase__ (							self							:	int   )	->       Union[str, Any]:
											# Initialize image_processing
											lowerCAmelCase   :List[Any]						=			self.image_processing_class(**self.image_processor_dict   )
											# create random numpy tensors
											lowerCAmelCase   :Union[str, Any]						=			prepare_image_inputs(self.image_processor_tester     , equal_resolution=UpperCAmelCase     , numpify=UpperCAmelCase   )
											for image in image_inputs:
																	self.assertIsInstance(UpperCAmelCase     , np.ndarray   )

											# Test not batched input
											lowerCAmelCase   :Union[str, Any]						=			image_processing(image_inputs[0]     , return_tensors='pt'   ).pixel_values
											self.assertEqual(
											    encoded_images.shape     , (
											        1,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.crop_size['height'],
											        self.image_processor_tester.crop_size['width'],
											    )     , )

											# Test batched
											lowerCAmelCase   :Any						=			image_processing(UpperCAmelCase     , return_tensors='pt'   ).pixel_values
											self.assertEqual(
											    encoded_images.shape     , (
											        self.image_processor_tester.batch_size,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.crop_size['height'],
											        self.image_processor_tester.crop_size['width'],
											    )     , )
					def        UpperCAmelCase__ (							self							:	Optional[int]   )	->       Optional[Any]:
											# Initialize image_processing
											lowerCAmelCase   :List[str]						=			self.image_processing_class(**self.image_processor_dict   )
											# create random PyTorch tensors
											lowerCAmelCase   :Optional[Any]						=			prepare_image_inputs(self.image_processor_tester     , equal_resolution=UpperCAmelCase     , torchify=UpperCAmelCase   )
											for image in image_inputs:
																	self.assertIsInstance(UpperCAmelCase     , torch.Tensor   )

											# Test not batched input
											lowerCAmelCase   :Any						=			image_processing(image_inputs[0]     , return_tensors='pt'   ).pixel_values
											self.assertEqual(
											    encoded_images.shape     , (
											        1,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.crop_size['height'],
											        self.image_processor_tester.crop_size['width'],
											    )     , )

											# Test batched
											lowerCAmelCase   :int						=			image_processing(UpperCAmelCase     , return_tensors='pt'   ).pixel_values
											self.assertEqual(
											    encoded_images.shape     , (
											        self.image_processor_tester.batch_size,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.crop_size['height'],
											        self.image_processor_tester.crop_size['width'],
											    )     , )







"""simple docstring"""


import argparse

import torch

from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging


logging.set_verbosity_info()


def        UpperCAmelCase      (					a__     ,      a__     ,      a__ ):





						'''simple docstring'''






						lowerCAmelCase   :Tuple						=			MobileBertConfig.from_json_file(a__ )
						print(F"""Building PyTorch model from configuration: {config}""" )
						lowerCAmelCase   :Tuple						=			MobileBertForPreTraining(a__ )
						# Load weights from tf checkpoint
						lowerCAmelCase   :Any						=			load_tf_weights_in_mobilebert(a__     ,      a__     ,      a__ )
						# Save pytorch-model
						print(F"""Save PyTorch model to {pytorch_dump_path}""" )
						torch.save(model.state_dict()     ,      a__ )


if __name__ == "__main__":
		__SCREAMING_SNAKE_CASE   =   argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
		)
		parser.add_argument(
		    '--mobilebert_config_file',
		    default=None,
		    type=str,
		    required=True,
		    help=(
		        'The config json file corresponding to the pre-trained MobileBERT model. \n'
		        'This specifies the model architecture.'
		    ),
		)
		parser.add_argument(
		    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
		)
		__SCREAMING_SNAKE_CASE   =   parser.parse_args()
		convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.mobilebert_config_file, args.pytorch_dump_path)